WO2018028253A1

WO2018028253A1 - Information processing method, apparatus and storage medium

Info

Publication number: WO2018028253A1
Application number: PCT/CN2017/082912
Authority: WO
Inventors: 肖非
Original assignee: 中兴通讯股份有限公司
Priority date: 2016-08-09
Filing date: 2017-05-03
Publication date: 2018-02-15
Also published as: CN107704471A

Abstract

An information processing method, apparatus and storage medium, the information processing method comprising: acquiring feature information of a target file; establishing a correspondence between the feature information and the target file; and classifying and storing the target file on the basis of the feature information and the correspondence. Using said solution, a picture or a video is stored according to feature information of the picture or video, which aids a user in quickly finding and browsing, improves user experience, and better fulfills user needs.

Description

Information processing method, device and storage medium

Cross-reference to related applications

The present application is based on a Chinese patent application filed on Apr. 10, 2016, the filing date of which is hereby incorporated by reference.

Technical field

The present disclosure relates to the field of communications, and in particular, to an information processing method, apparatus, and storage medium.

Background technique

With the popularity of smart phones, the use of smart phone photography, video capture and other functions is increasing, and even has gradually replaced digital cameras. Due to the portability of smartphones, more and more people are accustomed to using smartphones to record bits and pieces of life and work. However, when the existing pictures or videos are stored, they are stored in the order of shooting time, which is inconvenient for the user to search and browse, and the user experience is poor, which cannot meet the user's needs.

Summary of the invention

The technical problem to be solved by the embodiments of the present disclosure is to provide an information processing method, device, and storage medium. In the prior art, when a picture or video is stored, it is inconvenient for the user to search and browse, and the user experience is poor. Can not meet the needs of users.

To solve the above technical problem, an embodiment of the present disclosure provides an information processing method, including:

Obtaining feature information of the target file;

Establishing a correspondence between the feature information and the target file;

And classifying and storing the target file based on the feature information and the correspondence.

In the above solution, the acquiring the feature information of the target file includes: performing feature recognition on the image in the target file to obtain a feature recognition result; and determining feature information matching the feature recognition result according to a preset rule.

In the above solution, the determining the feature information that matches the feature recognition result according to the preset rule includes: searching for the feature recognition result in the preset biometric information template set, and obtaining the matching with the feature recognition result. The biometric information template is configured to find feature information corresponding to the biometric information template according to a correspondence between the preset biometric information template and the feature information.

In the above solution, the feature recognition result includes at least one of mouth angle information, lip information, upper and lower lip height information, shooting time information, shooting location information, and shooting weather information.

In the above solution, the method further includes:

Receiving call request information, where the call request information carries feature information of the target file;

Finding an object file corresponding to the feature information in the correspondence relationship;

Call the target file.

In order to solve the above technical problem, an embodiment of the present disclosure provides an information processing apparatus, including:

Obtaining a module, configured to obtain feature information of the target file;

a correspondence establishing module, configured to establish a correspondence between the feature information and the target file;

The saving module is configured to classify and store the target file based on the feature information and the correspondence.

In the above solution, the obtaining module includes:

a first extraction submodule configured to perform feature recognition on an image in the target file to obtain a feature recognition result;

Determining a submodule configured to determine, according to a preset rule, a match with the feature recognition result Feature information.

In the above solution, the determining submodule is configured to search for the feature recognition result in a preset biometric information template set, and obtain a biometric information template that matches the feature recognition result;

The feature information corresponding to the biometric information template is found according to a correspondence between the preset biometric information template and the feature information.

In the above solution, the device further includes:

a receiving module, configured to receive a call request, where the call request carries feature information of the target file;

a search module, configured to search for a target file corresponding to the feature information in the correspondence relationship;

Calling a module configured to make a call to the target file.

In order to solve the above technical problem, an embodiment of the present disclosure further provides a computer storage medium, where the computer storage medium stores computer executable instructions, and the computer executable instructions are used to execute the information processing method.

An advantageous aspect of the present disclosure is: an information processing method, apparatus, and storage medium according to an embodiment of the present disclosure, the information processing method includes acquiring feature information of an object file, where the target file includes a picture or a video; correspondingly, acquiring The feature information of the target file includes at least parsing the image feature information from the image of the image or the video; establishing a correspondence between the feature information and the target file, and correspondingly the feature information, the target file, and the corresponding Relationship preservation; using the above scheme, when storing pictures or videos, the pictures or videos are classified and stored according to the feature information of the pictures or videos, which facilitates the user to quickly find and browse, and improves the use. User experience, better meet the needs of users.

DRAWINGS

1 is a schematic flowchart of processing of an information processing method according to Embodiment 1 of the present disclosure;

2 is a schematic flowchart of processing of an information processing method according to Embodiment 2 of the present disclosure;

3 is a schematic structural diagram of a structure of an information processing apparatus according to Embodiment 3 of the present disclosure;

FIG. 4 is a schematic structural diagram of an information processing apparatus according to Embodiment 4 of the present disclosure.

detailed description

The embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.

Embodiment 1

The first embodiment provides an information processing method. The processing flow of the information processing method, as shown in FIG. 1 , includes the following steps:

Step S101: acquiring feature information of the target file;

Here, the target file includes: a picture or a video;

Correspondingly, the acquiring the feature information of the target file includes: performing feature recognition on the image in the target file to obtain a feature recognition result; and determining feature information that matches the feature recognition result according to a preset rule;

The determining, according to the preset rule, the feature information that matches the feature recognition result includes: searching for the feature recognition result in the preset biometric information template set, and obtaining a biometric information template that matches the feature recognition result. And searching for the feature information corresponding to the biometric information template according to the correspondence between the preset biometric information template and the feature information.

The feature recognition includes: biometric recognition and environmental feature recognition; the feature recognition result includes at least one of mouth angle information, lip information, upper and lower lip height information, shooting time information, shooting location information, and shooting weather information. .

The feature information includes at least image feature information, and the image feature information may include expression information of a person in a picture or a video image, such as laughing, angry, crying, etc., and the expression information may reflect the mood of the character, for example, laughing indicates that the mood is happy. .

The feature information may further include environment feature information. Correspondingly, the acquiring the feature information of the target file may further include: acquiring environment feature information of the current shooting environment when the picture or video is captured; and the environment feature information includes: shooting time information At least one of the shooting location information and the shooting weather information; the shooting location information can be obtained by a Global Positioning System (GPS) function of the mobile terminal (such as a mobile phone), and the GPS information is obtained through the network to obtain the shooting location information. . The weather information can be obtained by querying the network according to the shooting time information and shooting location information at the time of shooting.

The parsing the image feature information from the image of the image or the video comprises: performing face recognition on the image in the image or the image of the target frame in the video, and performing biometric information extraction on the recognized face image; The biometric information generates image feature information according to a preset rule.

The face recognition algorithm can be used to extract the biometric information of the face image successfully recognized in the face recognition. The extracted mouth angle information, lip information, and upper and lower lip height information can be reflected by the geometrical feature map of the mouth, including left and right. The corner feature points and upper and lower lip feature points, the center point of the geometric feature map, and the like.

The correspondence between the preset biometric information template and the image feature information may be saved as a correspondence table. For example, the correspondence table may be as shown in Table 1.

Table 1

It should be understood that in order to facilitate understanding of the solution of the embodiment, the biometric information in Table 1 The content of the template is simplified, and the actual biometric information template is divided by the geometrical feature information of the mouth in the facial expression feature information, including: left and right corner feature points and upper and lower lip feature points, each feature point has a position Coordinate values, according to these coordinate values, the height difference of the mouth angle up or down and the feature points of the upper and lower lips can be calculated. Different facial expression feature information, the corresponding corner angle and height difference are different; when smiling or laughing, the corner geometrical contour is raised, the corner angle and height difference are positive, and in a preset Within the threshold range; these facial expression feature information will be stored in the database according to different expression features in advance, that is, the biometric information template is stored in the database, and the correspondence table between the biometric information template and the image feature information is stored. Go to the database.

The geometric feature information extracted by the face recognition and the biometric information template pre-categorized in the database are compared one by one to obtain different similarity values, and the image feature information corresponding to the highest similarity value is taken as the current The expression of a person in a photo or video image, which is used as image feature information.

Before performing face recognition on the image of the target frame in the video, the method further includes: extracting the target frame from the compressed stored video file according to a preset manner.

Generally, in order to save storage space, the video is encoded and decoded by a compression algorithm such as H.264, and each frame of the compressed video does not necessarily have complete information, and usually is calculated according to the information of the preceding and succeeding frames. In order to restore the picture. Generally speaking, the frames of the video are divided into three types: I, B, and P, and information such as chromaticity and brightness is saved. Usually, the I frame includes complete image information, the B frame is the difference between the current frame and the previous frame information, and the P frame is the difference between the current frame and the previous frame information. The compressed video mainly reduces the size of the video by reducing the amount of information stored and transmitted by the difference between the current frame and the information before and after the B frame and the P frame are saved. If the picture is to be obtained through the B and P frames, the previous and subsequent frames are parsed, and the calculation amount is large. Therefore, in this embodiment, only the I frame is parsed. Compressed video is inserted in the fixed interval to ensure that the B and P frame images are restored without affecting the subsequent restored video due to the noise introduced by the preceding and succeeding frames. I frame of the entire picture information, the maximum interval is 300 frames (usually the frame rate is greater than 30 frames / sec). In this embodiment, after the video header information is parsed, the number of intervals of the I frame is obtained, and then all the I frames are parsed, that is, the target frame is extracted.

Step S102: Establish a correspondence between the feature information and the target file, and classify and store the target file based on the feature information and the correspondence relationship;

Here, after the image feature information of the picture or video is acquired, the correspondence between the image feature information and the picture or video is established.

For the same picture or video, in addition to establishing the correspondence between the image feature information and the picture or video, the correspondence between the shooting time information and/or the shooting location information and/or the shooting weather information may be established, and the shooting time information and / or shooting location information and / or shooting weather information, pictures or videos and correspondence saved.

For the same picture or video, if the image feature information, the shooting time information, the shooting location information, the corresponding relationship between the shooting weather information and the image are established, it may also be generated by generating a log, which may be saved to the mobile terminal. In the database (such as mobile phone), the user can choose to upload the log to the cloud, and the cloud database and the mobile terminal are consistently localized as a local backup of the mobile terminal. The default feature information of the log is saved by the data structure of the list linked list.

For example, get the time, location, weather, and mood information of the characters in the picture. Such as: July 10, Shanghai, sunny, happy. Corresponding relationship between the captured video or image and the corresponding time, place, weather, mood, etc., can be stored in the local database in the form of a log, and can also add information as needed, such as: safari, tourism, etc. And provide users to choose whether to upload the cloud, if the user did not upload at that time, the follow-up can also be uploaded at any time as needed. After the local log is deleted, you can also download and restore from the cloud, or delete the corresponding content in the cloud. Through the above scheme, the captured video or image can be automatically generated in time, place, weather, mood and other information by automatically generating text or expression packets and forming a log, which is convenient for the user to manage. Reason.

An information processing method according to an embodiment of the present disclosure, the information processing method includes acquiring feature information of an object file, where the target file includes a picture or a video, and acquiring the feature information of the target file includes at least parsing the image from the image of the image or the video. Feature information; establishing a correspondence relationship between the feature information and the target file, and saving the feature information, the object file, and the correspondence relationship; using the above scheme, when storing the image or the video, performing the image or video according to the feature information of the image or the video Classified storage makes it easy for users to quickly find and browse, which improves the user experience and better meets user needs.

Embodiment 2

The second embodiment provides an information processing method. The processing flow of the information processing method is as shown in FIG. 2, and includes the following steps:

Step S201: acquiring feature information of the target file;

Here, the target file includes: a picture or a video;

The feature recognition includes: biometric recognition and environmental feature recognition. The feature recognition result includes at least one of mouth angle information, lip information, upper and lower lip height information, shooting time information, shooting location information, and shooting weather information.

The feature information includes at least image feature information, and the image feature information may include a map The expression information of a person in a movie or a video image, such as laughing, angry, crying, etc., can reflect the mood of the character from the expression information, for example, the smile indicates that the mood is happy.

Table 1

It should be understood that, in order to facilitate understanding of the solution of the embodiment, the content of the biometric information template in Table 1 is simplified, and the actual biometric information template adopts a facial expression feature letter. The geometrical feature information of the mouth is divided into: the left and right corner feature points and the upper and lower lip feature points. Each feature point has a position coordinate value, and the mouth angle can be calculated according to the coordinate values, and the upper and lower lips can be calculated. The height difference of the feature points. Different facial expression feature information, the corresponding corner angle and height difference are different; when smiling or laughing, the corner geometrical contour is raised, the corner angle and height difference are positive, and in a preset Within the threshold range; these facial expression feature information will be stored in the database according to different expression features in advance, that is, the biometric information template is stored in the database, and the correspondence table between the biometric information template and the image feature information is stored to In the database.

Generally, in order to save storage space, the video is encoded and decoded by a compression algorithm such as H.264, and each frame of the compressed video does not necessarily have complete information, and usually is calculated according to the information of the preceding and succeeding frames. In order to restore the picture. Generally speaking, the frames of the video are divided into three types: I, B, and P, and information such as chromaticity and brightness is saved. Usually, the I frame includes complete image information, the B frame is the difference between the current frame and the previous frame information, and the P frame is the difference between the current frame and the previous frame information. The compressed video mainly reduces the size of the video by reducing the amount of information stored and transmitted by the difference between the current frame and the information before and after the B frame and the P frame are saved. If the picture is to be obtained through the B and P frames, the previous and subsequent frames are parsed, and the calculation amount is large. Therefore, in this embodiment, only the I frame is parsed. In order to ensure that the B and P frame images are not affected by the noise accumulation introduced by the preceding and succeeding frames during the parsing, the compressed video will be inserted into the I frame with the complete picture information in the fixed interval. The maximum interval is It is 300 frames (usually the frame rate is greater than 30 frames/second). This implementation After parsing the video header information, the number of intervals of the I frame is obtained, and then all the I frames are parsed, that is, the target frame is extracted.

Step S202: Establish a correspondence between the feature information and the target file, and store the target file based on the feature information and the corresponding relationship;

For example, get the time, location, weather, and mood information of the characters in the picture. Such as: July 10, Shanghai, sunny, happy. Corresponding relationship between the captured video or image and the corresponding time, place, weather, mood, etc., can be stored in the local database in the form of a log, and can also add information as needed, such as: safari, tourism, etc. And provide users to choose whether to upload the cloud, if the user did not upload at that time, the follow-up can also be uploaded at any time as needed. After the local log is deleted, you can also download and restore from the cloud, or delete the corresponding content in the cloud. Through the above scheme, the captured video or image can be automatically generated in time, place, weather, mood and other information to automatically generate text or expression packets and form a log, which is convenient for user management.

Step S203: Receive a call request, and carry the feature information of the target file in the call request;

Here, the object file includes a picture or a video, and the feature information includes at least image feature information. The feature information of the target file includes at least image feature information parsed from the image of the picture or the video, and the image feature information may include expression information of the person in the picture or the video image, such as laughing, angry, crying, etc., which may be reflected from the expression information. The mood of the character, such as laughing, indicates a happy mood. The feature information may further include at least one of shooting time information, shooting location information, and shooting weather information when the picture or video is captured.

Step S204: Find an object file corresponding to the feature information in the correspondence relationship;

In an embodiment, for the same picture or video, if the image feature information, the shooting time information, the shooting location information, the corresponding relationship between the shooting weather information and the image are established, the log may be generated in the form of a log, and the log may be saved. The default feature information is saved by the list linked list data structure. Subsequently, the log can be retrieved and queried according to the log information of the log, and the user can also add feature information according to his own needs, and edit and modify the feature information.

For example, you can query logs, photos, or videos by attributes such as time, location, and mood. For example, according to the time period 2016.6.1-2016.6.30, travel, happy mood and follow-up self-added keywords to query the corresponding log, photo or video. Because the log, photo or video also provides editing, modification, query, retrieval and other functions, it is convenient for urban white-collar workers to record the living status, and it is convenient for busy mothers to record the growth of children.

Step S205: calling the found target file;

In an embodiment, the found target file may be displayed, sent to a friend or family member, uploaded to a QQ space, a microblog, or a WeChat circle of friends.

A file invoking method according to an embodiment of the present disclosure, the file invoking method includes receiving an invoking request, and carrying the feature information of the object file in the invoking request; and according to the feature information and the correspondence relationship saved in the information processing method according to the first embodiment, Find the target text corresponding to the feature information Calling the found target file; using the above scheme, when the image or video is stored, the image or video is stored according to the feature information of the image or the video, which facilitates the user to quickly find and browse, and improves the user. Experience, better meet the needs of users.

Embodiment 3

The embodiment provides an information processing device. The composition of the information processing device, as shown in FIG. 3, includes:

The obtaining module 301 is configured to acquire feature information of the target file.

The target file includes a picture or a video.

The correspondence establishing module 302 is configured to establish a correspondence between the feature information and the target file.

The saving module 303 is configured to store the target file based on the feature information and the correspondence.

The feature information includes at least image feature information; the image feature information may include expression information of a person in a picture or a video image, such as laughing, angry, crying, etc., and the expression information may reflect the mood of the character, for example, laughing indicates that the mood is happy. . The feature information may also include environmental feature information. The obtaining module 301 is further configured to acquire environmental feature information of the current shooting environment when taking a picture or video. The environmental feature information includes at least one of shooting time information, shooting location information, and shooting weather information. The shooting location information can be obtained by the GPS function provided by the mobile terminal (such as a mobile phone), and the GPS information is obtained through the network to obtain the shooting location information. The weather information can be obtained by querying the network according to the shooting time information and shooting location information at the time of shooting.

The obtaining module 301 includes: an obtaining sub-module 3011 and a determining sub-module 3012. The obtaining sub-module 3011 is configured to perform feature recognition on an image in the target file to obtain a feature recognition result; the determining sub-module 3012, configured to determine, according to a preset rule, feature information that matches the feature recognition result; where the feature recognition includes: biometric identification And environmental feature recognition.

The determining sub-module 3012 is configured to search for the feature recognition result in the preset biometric information template set, and obtain a biometric information template that matches the feature recognition result; according to the preset biometric information template and feature Corresponding relationships between the information, and finding feature information corresponding to the biometric information template.

The feature recognition result includes at least one of mouth angle information, lip information, upper and lower lip height information, shooting time information, shooting location information, and shooting weather information.

Table 1

It should be understood that, in order to facilitate understanding of the solution of the embodiment, the content of the biometric information template in Table 1 is simplified, and the actual biometric information template is divided by the geometrical feature information of the mouth in the facial expression feature information. Including: left and right corner feature points and upper and lower lip feature points, each feature point has a position coordinate value, according to which the height difference of the mouth angle up or down and the upper and lower lip feature points can be calculated. Different facial expression feature information, the corresponding corner angle and height difference are different; when smiling or laughing, the corner geometrical contour is raised, the corner angle and height difference are positive, and in a preset Within the threshold range; these facial expression feature information will be stored in the database according to different expression features in advance. The biometric information template is stored in the database, and the correspondence table between the biometric information template and the image feature information is stored in the database.

The method further includes a second extraction module 304, configured to extract a target frame from the compressed stored video file according to a preset manner.

Generally, in order to save storage space, the video is encoded and decoded by a compression algorithm such as H.264, and each frame of the compressed video does not necessarily have complete information, and usually is calculated according to the information of the preceding and succeeding frames. In order to restore the picture. Generally speaking, the frames of the video are divided into three types: I, B, and P, and information such as chromaticity and brightness is saved. Usually, the I frame includes complete image information, the B frame is the difference between the current frame and the previous frame information, and the P frame is the difference between the current frame and the previous frame information. The compressed video mainly reduces the size of the video by reducing the amount of information stored and transmitted by the difference between the current frame and the information before and after the B frame and the P frame are saved. If the picture is to be obtained through the B and P frames, the previous and subsequent frames are parsed, and the calculation amount is large. Therefore, in this embodiment, only the I frame is parsed. In order to ensure that the B and P frame images are not affected by the noise accumulation introduced by the preceding and succeeding frames during the parsing, the compressed video will be inserted into the I frame with the complete picture information in the fixed interval. The maximum interval is It is 300 frames (usually the frame rate is greater than 30 frames/second). In this embodiment, after the video header information is parsed, the number of intervals of the I frame is obtained, and then all the I frames are parsed, that is, the target frame is extracted.

After acquiring the image feature information of the picture or video, the obtaining module 301 establishes a correspondence between the image feature information and the picture or video, and the saving module 303 saves the image feature information, the picture or the video, and the corresponding relationship.

For the same picture or video, in addition to establishing the correspondence between image feature information and pictures or videos The relationship, the correspondence relationship establishing module 302 can also establish a correspondence relationship between the shooting time information and/or the shooting location information and/or the shooting weather information, and the saving module 303 will take the shooting time information and/or the shooting location information and/or the shooting weather information. , pictures or videos and correspondences are saved.

For the same picture or video, if the image feature information, the shooting time information, the shooting location information, and the corresponding relationship between the shooting weather information and the image are established, the log may be saved in the save module 303, and the user may The log is uploaded to the cloud, and the cloud database and the save module 303 are consistent. The default feature information of the log is saved by the data structure of the list linked list.

For example, the acquisition module 301 acquires the time, location, weather, and mood information of the person in the picture when the picture is taken. Such as: July 10, Shanghai, sunny, happy. The correspondence relationship establishing module 302 associates the captured video or image with the corresponding time, location, weather, mood, and the like, and may be saved in the save module 303 in the form of a log, and may also add information as needed, such as: Zoo, travel, etc. And provide users to choose whether to upload the cloud, if the user did not upload at that time, the follow-up can also be uploaded at any time as needed. After the local log is deleted, you can also download and restore from the cloud, or delete the corresponding content in the cloud. Through the above scheme, the captured video or image can be automatically generated in time, place, weather, mood and other information to automatically generate text or expression packets and form a log, which is convenient for user management.

An information processing apparatus according to an embodiment of the present disclosure includes: the acquisition module 301 acquires feature information of an object file, the object file includes a picture or a video, and the feature information of the acquired object file includes at least an image from the picture or video. The image feature information is parsed; the correspondence relationship establishing module 302 establishes a correspondence relationship between the feature information and the target file, and the saving module 303 saves the feature information, the object file, and the corresponding relationship; and when the image or video is stored, according to the above scheme, The feature information of the picture or video is classified and stored in the picture or video, which facilitates the user to quickly find and browse, improves the user experience, and better meets the user's needs.

Embodiment 4

The embodiment provides an information processing device. The composition of the information processing device is as shown in FIG. 4, and includes:

The target file includes a picture or a video.

The receiving module 304 is configured to receive a call request, and the feature information of the target file is carried in the call request.

The searching module 305 is configured to search for the target file corresponding to the feature information in the correspondence relationship.

The calling module 306 is configured to make a call to the target file.

For the same picture or video, if image feature information, shooting time information, shooting location information, shooting weather information and the corresponding relationship of the image are established, it is also possible to generate a log. The form is saved, and the default feature information of the log is saved by the data structure of the list linked list. Subsequently, the log can be retrieved and queried according to the log information of the log, and the user can also add feature information according to his own needs, and edit and modify the feature information.

The calling module 306 is configured to invoke the target file found by the searching module 305;

The obtaining module 301 includes: an obtaining sub-module 3011 and a determining sub-module 3012. The obtaining sub-module 3011 is configured to perform feature recognition on an image in the target file to obtain a feature recognition result; the determining sub-module 3012. The method is configured to determine, according to a preset rule, feature information that matches the feature recognition result; where the feature recognition includes: biometric recognition and environmental feature recognition.

The face recognition algorithm can be used to extract the biometric information of the face image successfully recognized in the face recognition. The extracted mouth angle information, lip information, and upper and lower lip height information can be reflected by the geometrical feature map of the mouth, including left and right. Mouth corner feature points and upper and lower lip feature points, geometry Data such as the center point of the feature map.

Table 1

It should be understood that, in order to facilitate understanding of the solution of the embodiment, the content of the biometric information template in Table 1 is simplified, and the actual biometric information template is divided by the geometrical feature information of the mouth in the facial expression feature information. Including: left and right corner feature points and upper and lower lip feature points, each feature point has a position coordinate value, according to which the height difference of the mouth angle up or down and the upper and lower lip feature points can be calculated. Different facial expression feature information, the corresponding corner angle and height difference are different; when smiling or laughing, the corner geometrical contour is raised, the corner angle and height difference are positive, and in a preset Within the threshold range; these facial expression feature information will be stored in the database according to different expression features in advance, that is, the biometric information template is stored in the database, and the correspondence table between the biometric information template and the image feature information is stored to In the database.

Generally, in order to save storage space, the video is encoded and decoded by a compression algorithm such as H.264, and each frame of the compressed video does not necessarily have complete information. It is often necessary to calculate the image based on the previous and subsequent frames to restore the image. Generally speaking, the frames of the video are divided into three types: I, B, and P, and information such as chromaticity and brightness is saved. Usually, the I frame includes complete image information, the B frame is the difference between the current frame and the previous frame information, and the P frame is the difference between the current frame and the previous frame information. The compressed video mainly reduces the size of the video by reducing the amount of information stored and transmitted by the difference between the current frame and the information before and after the B frame and the P frame are saved. If the picture is to be obtained through the B and P frames, the previous and subsequent frames are parsed, and the calculation amount is large. Therefore, in this embodiment, only the I frame is parsed. In order to ensure that the B and P frame images are not affected by the noise accumulation introduced by the preceding and succeeding frames during the parsing, the compressed video will be inserted into the I frame with the complete picture information in the fixed interval. The maximum interval is It is 300 frames (usually the frame rate is greater than 30 frames/second). In this embodiment, after the video header information is parsed, the number of intervals of the I frame is obtained, and then all the I frames are parsed, that is, the target frame is extracted.

For the same picture or video, in addition to establishing the correspondence between the image feature information and the picture or video, the correspondence relationship establishing module 302 can also establish a correspondence relationship between the shooting time information and/or the shooting location information and/or the shooting weather information, and save The module 303 saves the shooting time information and/or shooting location information and/or shooting weather information, pictures or videos, and correspondences.

For example, the acquisition module 301 acquires the time, location, weather, and mood information of the person in the picture when the picture is taken. Such as: July 10, Shanghai, sunny, happy. Correspondence relationship establishment The module 302 associates the captured video or image with the corresponding time, location, weather, mood, and the like, and may be saved in the save module 303 in the form of a log, and may also add information as needed, such as: safari, travel Wait. And provide users to choose whether to upload the cloud, if the user did not upload at that time, the follow-up can also be uploaded at any time as needed. After the local log is deleted, you can also download and restore from the cloud, or delete the corresponding content in the cloud. Through the above scheme, the captured video or image can be automatically generated in time, place, weather, mood and other information to automatically generate text or expression packets and form a log, which is convenient for user management.

In another embodiment, a computer storage medium is stored, the computer storage medium storing computer executable instructions for executing the information processing method described above.

The obtaining module 301, the correspondence establishing module 302, the saving module 303, the receiving module 304, the searching module 305, and the calling module 306 in the information processing apparatus proposed in the embodiment of the present disclosure may all be implemented by a processor, and may also be specifically The logic circuit is implemented; wherein the processor may be a server located on an electronic device, and in practical applications, the processor may be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or Field programmable gate array (FPGA), etc.

Obviously, those skilled in the art should understand that the modules or steps of the above embodiments of the present disclosure may be implemented by a general computing device, which may be concentrated on a single computing device or distributed among multiple computing devices. On the network, optionally, they may be implemented by program code executable by the computing device, such that they may be stored in a storage medium (ROM/RAM, disk, optical disk) by a computing device, and in some In this case, the steps shown or described may be performed in an order different from that herein, or they may be separately fabricated into individual integrated circuit modules, or a plurality of the modules or steps may be implemented as a single integrated circuit module. Therefore, the present disclosure is not limited to any specific combination of hardware and software.

The above content is a further detailed description of the embodiments of the present disclosure in conjunction with the specific embodiments, and the specific implementation of the present disclosure is not limited to the description. For the technology to which the present disclosure pertains A person skilled in the art can make several simple deductions or substitutions without departing from the scope of the present disclosure, and should be considered as the scope of protection of the present disclosure.

Industrial applicability

The information processing method provided by the embodiment of the present disclosure acquires feature information of the target file, establishes a correspondence between the feature information and the target file, and classifies and stores the target file based on the feature information and the correspondence relationship; The above solution is used to store pictures or videos according to the feature information of the pictures or videos, so that the users can quickly find and browse, which improves the user experience and better meets the user's needs.

Claims

An information processing method, the method comprising:

Obtaining feature information of the target file;

Establishing a correspondence between the feature information and the target file;

And classifying and storing the target file based on the feature information and the correspondence.
The information processing method according to claim 1, wherein the acquiring the feature information of the target file comprises:

Performing feature recognition on the image in the target file to obtain a feature recognition result;

The feature information matching the feature recognition result is determined according to a preset rule.
The information processing method according to claim 2, wherein the determining the feature information that matches the feature recognition result according to the preset rule comprises:

Finding the feature recognition result in a preset biometric information template set, and obtaining a biometric information template that matches the feature recognition result;

The feature information corresponding to the biometric information template is found according to a correspondence between the preset biometric information template and the feature information.
The information processing method according to claim 2, wherein the feature recognition result includes at least one of mouth angle information, lip information, upper and lower lip height information, photographing time information, photographing location information, and photographing weather information.
The information processing method according to claim 1, wherein the method further comprises:

Receiving call request information, where the call request information carries feature information of the target file;

Finding an object file corresponding to the feature information in the correspondence relationship;

Make a call to the target file.
An information processing apparatus comprising:

Obtaining a module, configured to obtain feature information of the target file;

Corresponding relationship establishing module, configured to establish a pair of the feature information and the target file Should be related;

The saving module is configured to classify and store the target file based on the feature information and the correspondence.
The information processing device according to claim 6, wherein the acquisition module comprises:

a first extraction submodule configured to perform feature recognition on an image in the target file to obtain a feature recognition result;

Determining a sub-module configured to determine feature information that matches the feature recognition result according to a preset rule.
The information processing apparatus according to claim 7, wherein the determining submodule is configured to search for the feature recognition result in a preset biometric information template set, and obtain biometric information that matches the feature recognition result. template;

The feature information corresponding to the biometric information template is found according to a correspondence between the preset biometric information template and the feature information.
The information processing device according to claim 7, wherein the feature recognition result includes at least one of mouth angle information, lip information, upper and lower lip height information, photographing time information, photographing location information, and photographing weather information.
The information processing device of claim 6, wherein the device further comprises:

a receiving module, configured to receive a call request, where the call request carries feature information of the target file;

a search module, configured to search for a target file corresponding to the feature information in the correspondence relationship;

Calling a module configured to make a call to the target file.
A computer storage medium storing computer executable instructions for executing the routing information processing method according to any one of claims 1 to 5.